Search Results for author:
Found 16 papers, 5 papers with code
Meta-Learning Initializations for Image Segmentation
We extend first-order model agnostic meta-learning algorithms (including FOMAML and Reptile) to image segmentation, present a novel neural network architecture built for fast learning which we call EfficientLab, and leverage a formal definition of the test error of meta-learning algorithms to decrease error on out of distribution tasks.
Ranked #3 on
Few-Shot Semantic Segmentation
on FSS-1000
AutoMATES: Automated Model Assembly from Text, Equations, and Software
Models of complicated systems can be represented in different ways - in scientific papers, they are represented using natural language text as well as equations.
Federated Reconnaissance: Efficient, Distributed, Class-Incremental Learning
We describe federated reconnaissance, a class of learning problems in which distributed clients learn new concepts independently and communicate that knowledge efficiently.
Validity Assessment of Legal Will Statements as Natural Language Inference
This work introduces a natural language inference (NLI) dataset that focuses on the validity of statements in legal wills.
Texture Generation Using A Graph Generative Adversarial Network And Differentiable Rendering
Novel photo-realistic texture synthesis is an important task for generating novel scenes, including asset generation for 3D simulations.
WorldTree: A Corpus of Explanation Graphs for Elementary Science Questions supporting Multi-Hop Inference
Developing methods of automated inference that are able to provide users with compelling human-readable justifications for why the answer to a question is correct is critical for domains such as science and medicine, where user trust and detecting costly errors are limiting factors to adoption.
Composition by Conversation
Most musical programming languages are developed purely for coding virtual instruments or algorithmic compositions.
Learning what to read: Focused machine reading
In this work, we introduce a focused reading approach to guide the machine reading of biomedical literature towards what literature should be read to answer a biomedical query as efficiently as possible.
An Infinite Hidden Markov Model With Similarity-Biased Transitions
We describe a generalization of the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) which is able to encode prior information that state transitions are more likely between "nearby" states.
Bayesian Inference of Recursive Sequences of Group Activities from Tracks
We present a probabilistic generative model for inferring a description of coordinated, recursively structured group activities at multiple levels of temporal granularity based on observations of individuals' trajectories.
Inter-sentence Relation Extraction for Associating Biological Context with Events in Biomedical Texts
We present an analysis of the problem of identifying biological context and associating it with biochemical events in biomedical texts.
Blending Autonomous Exploration and Apprenticeship Learning
We present theoretical and empirical results for a framework that combines the benefits of apprenticeship and autonomous reinforcement learning.
Neural Architectures for Biological Inter-Sentence Relation Extraction
We introduce a family of deep-learning architectures for inter-sentence relation extraction, i. e., relations where the participants are not necessarily in the same sentence.
Learning Open Domain Multi-hop Search Using Reinforcement Learning
We propose a method to teach an automated agent to learn how to search for multi-hop paths of relations between entities in an open domain.
Neural Machine Translation for Code Generation
In the literature, a variety of different input scenarios have been explored, including generating code based on natural language description, lower-level representations such as binary or assembly (neural decompilation), partial representations of source code (code completion and repair), and source code in another language (code translation).
3DTextureTransformer: Geometry Aware Texture Generation for Arbitrary Mesh Topology
Learning to generate textures for a novel 3D mesh given a collection of 3D meshes and real-world 2D images is an important problem with applications in various domains such as 3D simulation, augmented and virtual reality, gaming, architecture, and design.
